'''simple docstring'''





import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
SCREAMING_SNAKE_CASE_:	Dict    =data_utils.TransfoXLTokenizer
SCREAMING_SNAKE_CASE_:	Union[str, Any]    =data_utils.TransfoXLCorpus
SCREAMING_SNAKE_CASE_:	Union[str, Any]    =data_utils
SCREAMING_SNAKE_CASE_:	Dict    =data_utils





def       lowerCAmelCase_							(   snake_case_			:					Union[str, Any]   ,       snake_case_			:					Optional[Any]   ,       snake_case_			:					List[Any]   ,       snake_case_			:					List[Any]     )       -> Any:


     '''simple docstring'''




     if transfo_xl_dataset_file:
          # Convert a pre-processed corpus (see original TensorFlow repo)
          with open(snake_case_   ,       "rb"     ) as fp:
               UpperCAmelCase_			       =  pickle.load(snake_case_   ,       encoding="latin1"     )
          # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
          UpperCAmelCase_			       =  pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
          print(f"""Save vocabulary to {pytorch_vocab_dump_path}"""     )
          UpperCAmelCase_			       =  corpus.vocab.__dict__
          torch.save(snake_case_   ,       snake_case_     )

          UpperCAmelCase_			       =  corpus.__dict__
          corpus_dict_no_vocab.pop("vocab"   ,       snake_case_     )
          UpperCAmelCase_			       =  pytorch_dump_folder_path + "/" + CORPUS_NAME
          print(f"""Save dataset to {pytorch_dataset_dump_path}"""     )
          torch.save(snake_case_   ,       snake_case_     )

     if tf_checkpoint_path:
          # Convert a pre-trained TensorFlow model
          UpperCAmelCase_			       =  os.path.abspath(snake_case_     )
          UpperCAmelCase_			       =  os.path.abspath(snake_case_     )

          print(f"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""     )
          # Initialise PyTorch model
          if transfo_xl_config_file == "":
               UpperCAmelCase_			       =  TransfoXLConfig()
          else:
               UpperCAmelCase_			       =  TransfoXLConfig.from_json_file(snake_case_     )
          print(f"""Building PyTorch model from configuration: {config}"""     )
          UpperCAmelCase_			       =  TransfoXLLMHeadModel(snake_case_     )

          UpperCAmelCase_			       =  load_tf_weights_in_transfo_xl(snake_case_   ,       snake_case_   ,       snake_case_     )
          # Save pytorch-model
          UpperCAmelCase_			       =  os.path.join(snake_case_   ,       snake_case_     )
          UpperCAmelCase_			       =  os.path.join(snake_case_   ,       snake_case_     )
          print(f"""Save PyTorch model to {os.path.abspath(snake_case_     )}"""     )
          torch.save(model.state_dict()   ,       snake_case_     )
          print(f"""Save configuration file to {os.path.abspath(snake_case_     )}"""     )
          with open(snake_case_   ,       "w"   ,       encoding="utf-8"     ) as f:
               f.write(config.to_json_string()     )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:	List[Any]    =argparse.ArgumentParser()
    parser.add_argument(
        '--pytorch_dump_folder_path',
        default=None,
        type=str,
        required=True,
        help='Path to the folder to store the PyTorch model or dataset/vocab.',
    )
    parser.add_argument(
        '--tf_checkpoint_path',
        default='',
        type=str,
        help='An optional path to a TensorFlow checkpoint path to be converted.',
    )
    parser.add_argument(
        '--transfo_xl_config_file',
        default='',
        type=str,
        help=(
            'An optional config json file corresponding to the pre-trained BERT model. \n'
            'This specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--transfo_xl_dataset_file',
        default='',
        type=str,
        help='An optional dataset file to be converted in a vocabulary.',
    )
    SCREAMING_SNAKE_CASE_:	List[str]    =parser.parse_args()
    convert_transfo_xl_checkpoint_to_pytorch(
        args.tf_checkpoint_path,
        args.transfo_xl_config_file,
        args.pytorch_dump_folder_path,
        args.transfo_xl_dataset_file,
    )





"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__:Optional[Any]									=			{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:Optional[Any]									=			["""ReformerTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:List[str]									=			["""ReformerTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:int									=			[
       """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """ReformerAttention""",
       """ReformerForMaskedLM""",
       """ReformerForQuestionAnswering""",
       """ReformerForSequenceClassification""",
       """ReformerLayer""",
       """ReformerModel""",
       """ReformerModelWithLMHead""",
       """ReformerPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE__:int									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
